Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services

Exploring the Possibilities of Artificial Intelligence and Big Data Techniques to Enhance Gamified Financial Services

María A. Pérez-Juárez, Javier M. Aguiar-Pérez, Miguel Alonso-Felipe, Javier Del-Pozo-Velázquez, Saúl Rozada-Raneros, Mikel Barrio-Conde
DOI: 10.4018/978-1-7998-8089-9.ch010
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A lot of millennials have been educated in gamified schools where they played Kahoot several times per week, and where applications like Classcraft made them feel like the protagonists of a videogame in which they had to accumulate points to be able to level up. All those that were educated in a gamified environment feel it is natural and logical that gamification is used in all areas. For this reason, gamification is increasingly becoming important in different fields including financial services, bringing new challenges. Gamification allows financial institutions to provide personalized and compelling experiences. Big data and artificial intelligence techniques are called to play an essential role in the gamification of financial services. This chapter aims to explore the possibilities of using artificial intelligence and big data techniques to support gamified financial services which are essential for digital natives but also increasingly important for digital immigrants.
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The Importance Of Gamification

Gamification refers to the use of gaming elements and procedures within non-game scenarios (Deterding et al, 2011). Gamification involves applying game design techniques, game mechanics, and/or game style to non-game situations to engage and motivate users and to facilitate them the solving of problems in an easy way.

Gamification is designed to be human-centred, using the principles of basic human psychology that tap into players’ needs through extrinsic (based on a reward) and intrinsic (based on a person’s genuine internal desire or feeling of enjoyment or happiness) motivation. As different authors highlight (Marczewski, 2015; Growth, 2016; Playmotiv, 2019), gamification elicits a physiological reaction releasing dopamine to induce feelings of accomplishment, pleasure and reward. This, in turn, motivates players to keep coming back.

Many researchers think that gamification makes it possible to offer improved and motivating applications and services to the user in very different domains (Werbach & Hunter, 2012; Zichermann & Cunningham, 2011; Zichermann & Linder, 2013).

Millennials are the first generation that grew up surrounded by technology. Millennials are the main smartphone and social media users, and have increasingly widespread and consolidated digital habits that have created a new way of relating to financial service providers. This generation demands highly engaging solutions. According to Justin (2018), gamification can create stickiness among millennial, whose financial behaviours are affected by the memories of the global financial crisis.

Key Terms in this Chapter

RegTech: It refers to the management of regulatory processes within the financial industry through technology.

Machine Learning: A learning technique that gives machines the ability to learn without being explicitly programmed. It is seen as a subset of Artificial Intelligence.

Big Data: It refers to the possibility of analyzing and systematically extracting information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Financial Inclusion: The degree to which individuals and businesses have access to useful and affordable financial products and services that meet their needs - transactions, payments, savings, credit and insurance - delivered in a responsible and sustainable way.

Deep Learning: Artificial Neural Networks and related Machine Learning algorithms that use multiple layers of neurons. It is seen as a subset of Machine Learning in Artificial Intelligence.

RoboAdvisor: It refers to digital applications that provide automated, algorithm-driven financial planning services with little to no human supervision.

Supervised Learning: It is a subcategory of Machine Learning (and Artificial Intelligence). It is characterized by the use of labelled datasets to train algorithms that classify data or predict results accurately.

Artificial Intelligence: A wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.

Financial Well-Being: A state in which people can fully meet current and ongoing financial obligations, and have control and feel secure about their financial future.

Fintech: It refers to financial technology, i.e., technology that seeks to enhance and automate the delivery and use of financial services.

Unsupervised Learning: It is a subcategory of Machine Learning (and Artificial Intelligence). It uses learning algorithms to analyze and cluster unlabelled datasets. These algorithms focus on discovering hidden patterns or data groupings without the need for human intervention.

Financial Literacy: The degree to which individuals have the capacity to understand and effectively use various financial skills, including personal financial management, budgeting, or investing.

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